Knowledge of a user is expanding exponentially via user interaction through knowledge supplying interactive portals, or communities exchanging information constantly over a network. Different systems and methods are proposed to maintain a knowledge graph of a user. In one mechanism, data in the form of a generic knowledge graph is stored in a remote database and the stored data can be retrieved by providing a query on a user electronic device. Identifying the data based on the query within the network may increase the network bandwidth usage. The data identified may not be locally relevant to the query or do not take current user context (such as location) and user knowledge into account. Further, the data stored in the remote database remains static until an entity such as an administrator or owner manually updates the data or with semi-automated support from deployed systems. Additionally, the knowledge graph data is not personal information of the user, but rather data is related to world entities in general.
In another mechanism, current knowledge graphs are entity based, i.e., they capture the relation between entities in the world. In traditional classification systems, supervised models are used where topics and their tokens are manually updated by the user. The user should continually update the supervised models for classification (i.e., classifying a web page). Further, the conventional methods and system may not differentiate between interest and knowledge and may not have a measurement when user's interest transforms into knowledge.
Thus there remains a need for a robust system and method for automatically constructing a knowledge graph of a user that captures the user's level of knowledge.
The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as Prior Art with regard to the present disclosure.